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Robust multi-user detection based on hybrid grey wolf optimization

Ji, Yuanfa and Fan, Z . and Sun, X. and Wang, S. and Yan, S. and Wu, S. and Fu, Q. and Kamarul Hawari, Ghazali (2020) Robust multi-user detection based on hybrid grey wolf optimization. In: Cognitive internet of things : frameworks, tools and aplications. Studies in Computational Intelligence (810). Springer International Publishing, Berlin, Germany, pp. 237-249. ISBN 9783030049454 (print), 9783030049461 (online)

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Abstract

The search for an effective nature-inspired optimization technique has certainly continued for decades. In this paper, a novel hybrid Grey wolf optimization and differential evolution algorithm robust multi-user detection algorithm is proposed to overcome the problem of high bit error rate (BER) in multi-user detection under impulse noise environment. The simulation results show that the iteration times of the multi-user detector based on the proposed algorithm is less than that of genetic algorithm, differential evolution algorithm and Grey wolf optimization algorithm, and has the lower BER.

Item Type: Book Section
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Grey wolf optimization algorithm; Differential evolution algorithm; Hybrid optimization; Multi-user detection; Impulse noise
Subjects: Q Science > QA Mathematics
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 04 Nov 2019 07:39
Last Modified: 04 Nov 2019 07:39
URI: http://umpir.ump.edu.my/id/eprint/25061
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